Abstract:
:In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture's focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations - making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to ∼+6%).
journal_name
Biol Cybernjournal_title
Biological cyberneticsauthors
Holzbach A,Cheng Gdoi
10.1007/s00422-014-0597-3subject
Has Abstractpub_date
2014-06-01 00:00:00pages
249-59issue
3eissn
0340-1200issn
1432-0770journal_volume
108pub_type
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